Efficient Schemes for Resource Allocation In the Cloud for Media Streaming Applications

A large number of users in the Internet have recently attracted by the media streaming applications. Due to advent of this bandwidth-intensive applications, with guaranteed QoS relying only on central resources at a media content provider it is economically inefficient to provide streaming distribution. media content providers(e.g., Video on Demand (VoD) providers) can use to obtain streaming resources that match the demand by the elastic infrastructure offered by the cloud computing. The amount of resources allocated (reserved) in the cloud are charged by the media content providers. Based on non-linear time-discount tariffs (e.g., Amazon Cloud Front and Amazon EC2) the existing cloud providers employ a pricing model for the reserved resources. This type of pricing scheme offers discount rates depending non-linearly on the period of time during which the resources are reserved in the cloud. In this case, the right amount of resources reserved in the cloud, and their reservation time are used to decided by the open problem such that the financial cost on the media content provider is minimized. A simple easy to implementalgorithm are proposed for resource reservation that maximally exploits discounted rates offered in the tariffs, while ensuring that as much as resources are reserved in the cloud. Our algorithm is carefully designed based on the prediction of demand for streaming capacity to reduce the risk of making wrong resource allocation decisions. The results show that the proposed algorithm significantly reduces the monetary cost of resource allocations in the cloud as compared to other conventional schemes according to our numerical evaluation and simulation.